Finance Based Pricing Advisor

A systematic approach to evaluate pricing changes financially:

1. Revenue Impact — How does this change ARPU/ARPA?

   • Direct revenue lift from price increase
   • Revenue loss from reduced conversion or increased churn
   • Net revenue impact

2. Conversion Impact — How does this affect trial-to-paid or sales conversion?

   • Higher prices may reduce conversion rate
   • Better packaging may improve conversion
   • Test assumptions

3. Churn Risk — Will existing customers leave due to price change?

   • Grandfathering strategy (protect existing customers)
   • Churn risk by segment (SMB vs. enterprise)
   • Churn elasticity (how sensitive are customers to price?)

4. Expansion Impact — Does this create or block expansion opportunities?

   • New premium tier = upsell path
   • Usage-based pricing = expansion as customers grow
   • Add-ons = cross-sell opportunities

5. CAC Payback Impact — Does pricing change affect unit economics?

   • Higher ARPU = faster payback
   • Lower conversion = higher effective CAC
   • Net effect on LTV:CAC ratio

Pricing Change Types

Direct monetization changes:

• Price increase (raise prices for all customers or new customers only)
• New premium tier (create upsell path)
• Paid add-on (monetize previously free feature)
• Usage-based pricing (charge for consumption)

Discount strategies:

• Annual prepay discount (improve cash flow)
• Volume discounts (larger deals)
• Promotional pricing (temporary price reduction)

Packaging changes:

• Feature bundling (combine features into tiers)
• Unbundling (separate features into add-ons)
• Pricing metric change (seats → usage, or vice versa)

Anti-Patterns (What This Is NOT)

• Not value-based pricing: This evaluates a proposed change, not "what should we charge?"
• Not WTP research: This analyzes impact, not "what will customers pay?"
• Not competitive positioning: This is financial analysis, not market positioning
• Not packaging architecture: This evaluates one change, not redesigning all tiers

When to Use This Framework

Use this when:

• You have a specific pricing change to evaluate (e.g., "Should we raise prices 20%?")
• You need to quantify revenue, churn, and conversion trade-offs
• You're deciding between pricing change options (test A vs. B)
• You need to present pricing change impact to leadership or board

Don't use this when:

• You're designing pricing strategy from scratch (use value-based pricing frameworks)
• You haven't validated willingness-to-pay (do customer research first)
• You don't have baseline metrics (ARPU, churn, conversion rates)
• Change is too small to matter (<5% price change, <10% of customers affected)

Facilitation Source of Truth

Use workshop-facilitation as the default interaction protocol for this skill.

It defines:

• session heads-up + entry mode (Guided, Context dump, Best guess)
• one-question turns with plain-language prompts
• progress labels (for example, Context Qx/8 and Scoring Qx/5)
• interruption handling and pause/resume behavior
• numbered recommendations at decision points
• quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.

Application

This interactive skill asks up to 4 adaptive questions, offering 3-5 enumerated options at decision points.

Step 0: Gather Context

Agent asks:

"Let's evaluate the financial impact of your pricing change. Please provide:

Current pricing:

• Current ARPU or ARPA
• Current pricing tiers (if applicable)
• Current monthly churn rate
• Current trial-to-paid conversion rate (if relevant)

Proposed pricing change:

• What change are you considering? (price increase, new tier, add-on, etc.)
• New pricing (if known)
• Affected customer segment (all, new only, specific tier)

Business context:

• Total customers (or MRR/ARR)
• CAC (to assess payback impact)
• NRR (to assess expansion context)

You can provide estimates if you don't have exact numbers."

Step 1: Identify Pricing Change Type

Agent asks:

"What type of pricing change are you considering?

1. Price increase — Raise prices for new customers, existing customers, or both
2. New premium tier — Add higher-priced tier with additional features
3. Paid add-on — Monetize a new or existing feature separately
4. Usage-based pricing — Charge for consumption (seats, API calls, storage, etc.)
5. Discount strategy — Annual prepay discount, volume pricing, or promotional pricing
6. Packaging change — Rebundle features, change pricing metric, or tier restructure

Choose a number, or describe your specific pricing change."

Based on selection, agent adapts questions:

If Option 1 (Price Increase):

Agent asks:

"Price increase details:

• Current price: $___
• New price: $___
• Increase: ___%

Who is affected?

1. New customers only (grandfather existing)
2. All customers (existing + new)
3. Specific segment (e.g., SMB only, new plan only)

When would this take effect?

• Immediately
• Next billing cycle
• Gradual rollout (test first)"

If Option 2 (New Premium Tier):

Agent asks:

"Premium tier details:

• Current top tier price: $___
• New premium tier price: $___
• Key features in premium tier: [list]

Expected adoption:

• What % of current customers might upgrade? ___%
• What % of new customers might choose premium? ___%

Cannibalization risk:

• Will premium tier cannibalize current top tier?"

If Option 3 (Paid Add-On):

Agent asks:

"Add-on details:

• Add-on name: ___
• Price: $___ /month or /user
• Currently free or new feature?

Expected adoption:

• What % of customers would pay for this? ___%
• Is this feature currently used (if free)?
• Will making it paid hurt retention?"

If Option 4 (Usage-Based Pricing):

Agent asks:

"Usage pricing details:

• Usage metric: (seats, API calls, storage, transactions, etc.)
• Pricing: $___ per [unit]
• Free tier or minimum? (e.g., first 1,000 API calls free)

Expected impact:

• Average customer usage: ___ units/month
• Expected ARPU change: $current → $new

Expansion potential:

• As customers grow usage, will ARPU increase?"

If Option 5 (Discount Strategy):

Agent asks:

"Discount details:

• Discount type: (annual prepay, volume, promotional)
• Discount amount: ___% off
• Duration: (ongoing, limited time)

Trade-off:

• Lower price vs. improved cash flow (annual prepay)
• Lower price vs. larger deal size (volume)
• Lower price vs. urgency (promotional)"

If Option 6 (Packaging Change):

Agent asks:

"Packaging change details:

• What are you changing? (bundling, unbundling, pricing metric)
• Current packaging: [describe]
• New packaging: [describe]

Expected impact:

• ARPU change: $current → $new
• Conversion change: ___% → ___%
• Churn risk: (low, medium, high)"

Step 2: Assess Expected Impact

Agent asks:

"Now let's quantify the impact. Based on your pricing change, estimate:

Revenue impact:

• Current ARPU: $___
• Expected new ARPU: $___
• ARPU lift: ___%

Conversion impact:

• Current conversion rate: ___%
• Expected new conversion rate: ___%
• Conversion change: [increase / decrease / no change]

Churn risk:

• Current monthly churn: ___%
• Expected churn after change: ___%
• Churn risk: [low / medium / high]

Expansion impact:

• Does this create expansion opportunities? (new tier to upgrade to, usage growth)
• Expected NRR change: ___% → ___%

You can provide estimates. We'll model scenarios (conservative, base, optimistic)."

Step 3: Evaluate Current State

Agent asks:

"To assess whether this pricing change makes sense, I need your current baseline:

Current metrics:

• MRR or ARR: $___
• Number of customers: ___
• ARPU/ARPA: $___
• Monthly churn rate: ___%
• NRR: ___%
• CAC: $___
• LTV: $___

Growth context:

• Current growth rate: ___% MoM or YoY
• Target growth rate: ___%

Competitive context:

• Are you priced below, at, or above market?
• Competitive pressure: (low, medium, high)"

Step 4: Deliver Recommendations

Agent synthesizes:

• Revenue impact (ARPU lift × customer base)
• Conversion impact (new customers affected)
• Churn impact (existing customers affected)
• Net revenue impact
• CAC payback impact
• Risk assessment

Agent offers 3-4 recommendations:

Recommendation Pattern 1: Implement Broadly

When:

• Net revenue impact clearly positive (>10% ARPU lift, <5% churn risk)
• Minimal conversion impact
• Strong value justification

Recommendation:

"Implement this pricing change — Strong financial case

Revenue Impact:

• Current MRR: $___
• ARPU lift: ___% ($current → $new)
• Expected MRR increase: +$/month (+%)

Churn Risk: Low

• Expected churn increase: ___% → % (+% points)
• Churn-driven MRR loss: -$___/month
• Net MRR impact: +$___/month ✅

Conversion Impact:

• Current conversion: ___%
• Expected conversion: % (% change)
• Impact on new customer acquisition: [minimal / manageable]

CAC Payback Impact:

• Current payback: ___ months
• New payback: ___ months (faster due to higher ARPU)

Why this works: [Specific reasoning based on numbers]

How to implement:

1. Grandfather existing customers (if raising prices)
   • Protect current base from churn
   • New pricing for new customers only
2. Communicate value
   • Emphasize features, outcomes, ROI
   • Justify price with value delivered
3. Monitor metrics (first 30-60 days)
   • Conversion rate (should stay within ___%)
   • Churn rate (should stay <___%)
   • Customer feedback

Expected timeline:

• Month 1: +$___ MRR from new customers
• Month 3: +$___ MRR (cumulative)
• Month 6: +$___ MRR
• Year 1: +$___ ARR

Success criteria:

• Conversion rate stays >___%
• Churn rate stays <___%
• NRR improves to >___%"

Recommendation Pattern 2: Test First (A/B Test)

When:

• Uncertain impact (wide range between conservative and optimistic)
• Moderate churn or conversion risk
• Large customer base (can test with subset)

Recommendation:

"Test with a segment before broad rollout — Impact is uncertain

Why test:

• ARPU lift estimate: ___% (wide confidence interval)
• Churn risk: Medium (___% → ___%)
• Conversion impact: Uncertain (___% → ___% estimated)

Test design:

Cohort A (Control):

• Current pricing: $___
• Size: ___% of new customers (or ___ customers)

Cohort B (Test):

• New pricing: $___
• Size: ___% of new customers (or ___ customers)

Duration: 60-90 days (need statistical significance)

Metrics to track:

• Conversion rate (A vs. B)
• ARPU (A vs. B)
• 30-day retention (A vs. B)
• 90-day churn (A vs. B)
• NRR (A vs. B)

Decision criteria:

Roll out broadly if:

• Conversion rate (B) >___% of control (A)
• Churn rate (B) <___% higher than control
• Net revenue (B) >___% higher than control

Don't roll out if:

• Conversion drops >___%
• Churn increases >___%
• Net revenue impact negative

Expected timeline:

• Week 1-2: Launch test
• Week 8-12: Enough data for statistical significance
• Month 3: Decision to roll out or kill

Risk: Medium. Test mitigates risk before broad rollout."

Recommendation Pattern 3: Modify Approach

When:

• Original proposal has significant risk
• Better alternative exists
• Need to adjust pricing change to improve outcomes

Recommendation:

"Modify your approach — Original proposal has risks

Original Proposal:

• [Price increase / New tier / Add-on / etc.]
• Expected ARPU lift: ___%
• Churn risk: High (___% → ___%)
• Net revenue impact: Uncertain or negative

Problem: [Specific issue: e.g., "20% price increase will likely cause 10% churn, wiping out revenue gains"]

Alternative Approach:

Option 1: Smaller price increase

• Instead of ___% increase, try ___%
• Lower churn risk (___% vs. ___%)
• Still positive net revenue: +$___/month

Option 2: Grandfather existing, raise for new only

• Protect current base (zero churn risk)
• Higher prices for new customers only
• Gradual ARPU improvement over time

Option 3: Value-based pricing (charge more for high-value segments)

• Keep SMB pricing flat
• Raise enterprise pricing ___%
• Lower churn risk (enterprise is stickier)

Recommended: [Specific option with reasoning]

Why this is better:

• Lower churn risk
• Comparable revenue upside
• Easier to communicate

How to implement: [Specific steps for alternative approach]"

Recommendation Pattern 4: Don't Change Pricing

When:

• Net revenue impact negative or marginal
• High churn risk without offsetting gains
• Competitive or strategic reasons to hold pricing

Recommendation:

"Don't change pricing — Risks outweigh benefits

Why:

• Expected revenue lift: +$/month (%)
• Expected churn impact: -$/month (%)
• Net revenue impact: -$___/month 🚨 or marginal

Problem: [Specific issue: e.g., "Churn-driven revenue loss exceeds price increase gains"]

What would need to change:

For price increase to work:

• Churn rate must stay below ___% (currently ___%)
• OR conversion rate must stay above ___% (currently ___%)
• OR you need to reduce CAC to offset lower conversion

Alternative strategies:

Instead of raising prices:

1. Improve retention — Reduce churn from ___% to ___% (same revenue impact as price increase, lower risk)
2. Expand within base — Increase NRR from ___% to ___% via upsells
3. Reduce CAC — More efficient acquisition (better than pricing)

When to revisit pricing:

• After improving retention (churn <___%)
• After validating willingness-to-pay (WTP research)
• After competitive landscape changes

Decision: Hold pricing for now, focus on [retention / expansion / acquisition efficiency]."

Step 5: Sensitivity Analysis (Optional)

Agent offers:

"Want to see what-if scenarios?

1. Optimistic case — Higher ARPU lift, lower churn
2. Pessimistic case — Lower ARPU lift, higher churn
3. Breakeven analysis — What churn rate makes this neutral?

Or ask any follow-up questions."

Agent can provide:

• Scenario modeling (optimistic/pessimistic/breakeven)
• Sensitivity tables (if churn is X%, revenue impact is Y)
• Comparison to alternative pricing strategies

Examples

See examples/ folder for sample conversation flows. Mini examples below:

Example 1: Price Increase (Good Case)

Scenario: 20% price increase for new customers only

Current state:

• ARPU: $100/month
• Customers: 1,000
• MRR: $100K
• Churn: 3%/month
• New customers/month: 50

Proposed change:

• New customer pricing: $120/month (+20%)
• Existing customers: Grandfathered at $100

Impact:

• New customer ARPU: $120 (+20%)
• Churn risk: Low (existing protected)
• Conversion impact: Minimal (<5% drop estimated)

Recommendation: Implement. Net revenue impact +$12K/year with low risk.

Example 2: Price Increase (Risky)

Scenario: 30% price increase for all customers

Current state:

• ARPU: $50/month
• Customers: 5,000
• MRR: $250K
• Churn: 5%/month (already high)

Proposed change:

• All customers: $65/month (+30%)

Impact:

• ARPU lift: +30% = +$75K MRR
• Churn risk: High (5% → 8% estimated)
• Churn-driven loss: 3% × 5,000 × $65 = -$9.75K MRR/month

Net impact: +$75K - $9.75K = +$65K MRR (but accelerating churn problem)

Recommendation: Don't change. Fix retention first (reduce 5% churn), then raise prices.

Example 3: New Premium Tier

Scenario: Add $500/month premium tier

Current state:

• Top tier: $200/month (500 customers)
• ARPA: $200

Proposed change:

• New tier: $500/month with advanced features
• Expected adoption: 10% of current top tier (50 customers)

Impact:

• Upsell revenue: 50 × ($500 - $200) = +$15K MRR
• Cannibalization risk: Low (features justify premium)
• NRR impact: Increases from 105% to 110%

Recommendation: Implement. Creates expansion path, minimal cannibalization risk.

Common Pitfalls

Pitfall 1: Ignoring Churn Impact

Symptom: "We'll raise prices 30% and make $X more!" (no churn modeling)

Consequence: Churn wipes out revenue gains. Net impact negative.

Fix: Model churn scenarios (conservative, base, optimistic). Factor churn-driven revenue loss into net impact.

Pitfall 2: Not Grandfathering Existing Customers

Symptom: "We're raising prices for everyone effective immediately"

Consequence: Massive churn spike from existing customers who feel betrayed.

Fix: Grandfather existing customers. Raise prices for new customers only.

Pitfall 3: Testing Without Statistical Power

Symptom: "We tested on 10 customers and it worked!"

Consequence: 10 customers isn't statistically significant. Results are noise.

Fix: Test with large enough sample (100+ customers per cohort) for 60-90 days.

Pitfall 4: Pricing Changes Without Value Justification

Symptom: "We're raising prices because we need more revenue"

Consequence: Customers see price increase without corresponding value increase. Churn.

Fix: Tie price increases to value improvements (new features, better support, outcomes delivered).

Pitfall 5: Ignoring CAC Payback Impact

Symptom: "Higher ARPU is always better!"

Consequence: If conversion drops 30%, effective CAC increases dramatically. Payback period explodes.

Fix: Calculate CAC payback impact. Higher ARPU with lower conversion might make payback worse, not better.

Pitfall 6: Annual Discounts That Hurt Margin

Symptom: "30% discount for annual prepay!" (improves cash but destroys LTV)

Consequence: Customers lock in low prices for a year. Revenue per customer decreases.

Fix: Limit annual discounts to 10-15%. Balance cash flow improvement with LTV protection.

Pitfall 7: Copycat Pricing (Competitor-Based)

Symptom: "Competitor raised prices, so should we"

Consequence: Your customers, value prop, and cost structure are different. What works for them may not work for you.

Fix: Use competitors as data points, not decisions. Make pricing decisions based on your unit economics.

Pitfall 8: Premature Optimization

Symptom: "Let's A/B test 47 different price points!"

Consequence: Analysis paralysis. Spending months on 5% pricing optimizations while missing 50% growth opportunities elsewhere.

Fix: Big pricing changes (tiers, packaging, add-ons) matter more than micro-optimizations. Start there.

Pitfall 9: Forgetting Expansion Revenue

Symptom: "We're maximizing ARPU at acquisition"

Consequence: High upfront pricing prevents landing customers. Miss expansion opportunities.

Fix: Consider "land and expand" strategy. Lower entry price, higher expansion revenue via upsells.

Pitfall 10: No Pricing Change Communication Plan

Symptom: "We're raising prices next month" (no customer communication)

Consequence: Surprised customers churn. Poor reviews. Reputation damage.

Fix: Communicate pricing changes 30-60 days in advance. Emphasize value, not just price.

References

Related Skills

• saas-revenue-growth-metrics — ARPU, ARPA, churn, NRR metrics used in pricing analysis
• saas-economics-efficiency-metrics — CAC payback impact of pricing changes
• finance-metrics-quickref — Quick lookup for pricing-related formulas
• feature-investment-advisor — Evaluates whether to build features that enable pricing changes
• business-health-diagnostic — Broader business context for pricing decisions

External Frameworks (Comprehensive Pricing Strategy)

These are OUTSIDE the scope of this skill but relevant for broader pricing work:

• Value-Based Pricing — Price based on value delivered, not cost
• Van Westendorp Price Sensitivity — WTP research methodology
• Conjoint Analysis — Feature-to-price trade-off research
• Good-Better-Best Packaging — Tier architecture design
• Price Anchoring & Decoy Pricing — Psychological pricing tactics
• Patrick Campbell (ProfitWell): Pricing research and benchmarks

Future Skills (Comprehensive Pricing)

For topics NOT covered here, see future pricing-strategy-suite:

• value-based-pricing-framework — How to price based on value
• willingness-to-pay-research — WTP research methods
• packaging-architecture-advisor — Tier and bundle design
• pricing-psychology-guide — Anchoring, decoys, framing
• monetization-model-advisor — Seat-based vs. usage vs. outcome pricing

Provenance

• Adapted from research/finance/Finance_For_PMs.Putting_It_Together_Synthesis.md (Decision Framework #3)
• Pricing scenarios from research/finance/Finance for Product Managers.md